Vehicle re-identification using multi-task deep learning network and spatio-temporal model

Vehicle re-identification (re-ID) plays an important role in the automatic analysis of the increasing urban surveillance videos and has become a hot topic in recent years. Vehicle re-ID aims at identifying vehicles across different cameras. However, it suffers from the difficulties caused by various viewpoint of vehicles, diversified illuminations, and complicated environments. In this paper, a two-stage vehicle re-ID framework is proposed to address these challenges, which contains a feature extraction module for achieving discriminative features and a spatial-temporal re-ranking module to improve the accuracy of vehicle re-ID task. Firstly, a multi-task deep network that integrates identity predicting network, attribute recognition network and verification network is adopted to learn discriminate features. Secondly, a spatio-temporal model is built to re-rank the appearance information measurement results, which utilizes the spatio-temporal relationship to increase constraints of the images. Moreover, to facilitate progressive vehicle re-ID research, experiments are conducted on both the VeRi-776 dataset and VehicleID dataset. Both the proposed multi-task feature extraction module and spatio-temporal model achieve considerable improvements.